11 research outputs found

    Novel Semi-Supervised Learning Models to Balance Data Inclusivity and Usability in Healthcare Applications

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    abstract: Semi-supervised learning (SSL) is sub-field of statistical machine learning that is useful for problems that involve having only a few labeled instances with predictor (X) and target (Y) information, and abundance of unlabeled instances that only have predictor (X) information. SSL harnesses the target information available in the limited labeled data, as well as the information in the abundant unlabeled data to build strong predictive models. However, not all the included information is useful. For example, some features may correspond to noise and including them will hurt the predictive model performance. Additionally, some instances may not be as relevant to model building and their inclusion will increase training time and potentially hurt the model performance. The objective of this research is to develop novel SSL models to balance data inclusivity and usability. My dissertation research focuses on applications of SSL in healthcare, driven by problems in brain cancer radiomics, migraine imaging, and Parkinson’s Disease telemonitoring. The first topic introduces an integration of machine learning (ML) and a mechanistic model (PI) to develop an SSL model applied to predicting cell density of glioblastoma brain cancer using multi-parametric medical images. The proposed ML-PI hybrid model integrates imaging information from unbiopsied regions of the brain as well as underlying biological knowledge from the mechanistic model to predict spatial tumor density in the brain. The second topic develops a multi-modality imaging-based diagnostic decision support system (MMI-DDS). MMI-DDS consists of modality-wise principal components analysis to incorporate imaging features at different aggregation levels (e.g., voxel-wise, connectivity-based, etc.), a constrained particle swarm optimization (cPSO) feature selection algorithm, and a clinical utility engine that utilizes inverse operators on chosen principal components for white-box classification models. The final topic develops a new SSL regression model with integrated feature and instance selection called s2SSL (with “s2” referring to selection in two different ways: feature and instance). s2SSL integrates cPSO feature selection and graph-based instance selection to simultaneously choose the optimal features and instances and build accurate models for continuous prediction. s2SSL was applied to smartphone-based telemonitoring of Parkinson’s Disease patients.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

    Effects of balance training on balance, gait and non-motor symptoms in individuals with Parkinson's disease

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    Postural instability (PI) is one of the most disabling symptoms of Parkinson’s disease (PD). PI is a well-known risk factor for falls in individuals with PD that worsens with disease progression. About 50-70% of people with PD fall once or more in a year, which is much higher than the 30% fall rate reported for community dwelling older individuals. Impaired balance associated with PI and fear of falling are factors related to decreased mobility and poor quality of life in individuals with PD. Several studies have examined the effects of exercise particularly strengthening and aerobic training on various motor and non-motor symptoms of PD. However to date, few studies have examined effects of balance specific interventions on balance, spatiotemporal gait, and non-motor symptoms such as fatigue, pain and depression. Moreover, none have used a commercially available device, Biodex Balance System (BSS) to implement a challenging balance training protocol. BSS consists of a moving platform that can be used to progressively challenge one’s balance while providing visual feedback. Finally, most of the previous studies did not report information pertaining to clinically meaningful changes in balance and its implications to physical function and quality of life in individuals with PD. The overall objective of this study was to evaluate whether short term progressively challenging balance specific training using the BSS improves balance, spatiotemporal gait and non-motor symptoms including fatigue, pain and depression in individuals with PD compared to usual non-progressive balance exercises. The central hypothesis is that challenging balance exercises, where individuals with PD are challenged out of their comfort zone for static and dynamic balance can significantly improve balance and spatiotemporal gait. Chapter 2 describes aims 1 and 2, utilizing 4 weeks of BBS balance training to determine changes in sway measures and spatio-temporal gait variables in individuals with PD. Ten individuals in a balance exercise group using the BSS and 10 individuals in general balance exercise group without Biodex (Non-BSS) completed the study. This study showed that 4 weeks of balance exercises using BSS resulted in significant within group improvement in sway area, center of pressure (CoP), path length in antero-posterior (AP) direction in the BSS group. We also found significant within group improvements in the balance measured by Berg Balance Scale, gait velocity, and step length in both groups. Additionally, we found significant within group improvements in functional scores measured by the Timed Up and Go and 6 Minute Walk Test in both groups. However, we did not find significant between group differences for any of the outcome variables. Due to technical failure in the system, we were not able to report force plate data from the non-BSS group. Chapter 3 describes aim 3, where 4 weeks of BSS training was utilized to determine changes in fatigue, pain, depression, fear of falling and quality of life in individuals with PD. Although motor symptoms of PD are described widely in the literature, and several studies report improvement in motor symptoms following various exercise trainings, little has been done to determine the efficacy of exercise interventions on the non - motor symptoms of PD. Aerobic exercise, strengthening, gait, tai-chi, qigong, and yoga therapy have been shown to improve motor deficits in PD. However, no study has examined the effects of balance training with BSS on non-motor features such as depression, fatigue, pain and fear of falling in individuals with PD. In our study, we determined the effects of balance training on non - motor symptoms of PD. The results demonstrated that 4 weeks of balance training resulted in a non-significant trend toward improvement in depression, pain, and fear of falling, and only the BSS training group demonstrated statistically significant improvement in fatigue. In summary, this dissertation work provides evidence that the use of the BSS is feasible, safe, and effective in improving balance, gait, and function in individuals with PD. However further study with a larger sample size, randomized control design, and biomechanical (force plate) data in both groups is required to better understand the role of challenging balance training in this population. The findings of this dissertation work have implications about designing future studies with specific intensity of balance exercises needed to make meaningful changes in balance, gait and non-motor symptoms of not only the individuals with PD but also in individuals with other neurological disorders resulting in PI

    The QuickSort: A brief screen for detecting cognitive impairment in older adults

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    The prevalence of neurodegenerative disorders, especially dementia, is increasing as the population ages (Hou et al., 2019; World Health Organisation, 2021). There are currently no cures for dementia, but early treatments and interventions may slow disease progression and improve quality of life (Liss et al., 2021; Livingston et al., 2020). Despite early declines in memory and executive functioning (Erkkinen et al., 2018), dementia continues to be poorly detected (Amjad et al., 2018; Lang et al., 2017; Walker et al., 2017). The challenges in detecting dementia early are examined in Chapter 1, including reports of cognitive decline being unforthcoming or inaccurate and clinicians having limited time to conduct cognitive assessments (Olivari et al., 2020; Pink et al., 2018). Consequently, cognitive screens are recommended to detect cognitive decline quickly and objectively (Ismail Z et al., 2020; Pink et al., 2018). Chapter 1 examines how cognitive screens can expedite the assessments that are required to diagnose dementia (Roebuck-Spencer et al., 2017), facilitate access to interventions (Pink et al., 2018), and help identify older adults who are at risk of experiencing difficulties with independent functioning, decision-making, mental health and wellbeing (Ahlqvist et al., 2016). Chapter 2 evaluates some of the most popular cognitive screens and recognises that they are less accurate for detecting cognitive decline than more time-intensive and comprehensive neuropsychological assessments (Summers et al., 2019). Neuropsychological assessments often examine executive functioning by administering tasks involving response inhibition, such as sorting tests (Wallace et al., 2022), which can detect dementia and MCI (Rabi et al., 2020). However, sorting tests are rarely used for screening purposes (Hobson, 2007). In reviewing the common cognitive screens, such as the Mini Mental Status Examination and Montreal Cognitive Assessment, Chapter 2 notes they do not include sorting tasks, and are limited by their administration and scoring time, user-friendliness, availability, reliability, and ability to detect cognitive decline (Hemmy et al., 2020; Larner, 2013). Although some sorting tests are quick to administer and provide a promising alternative to common cognitive screens, they often use materials that are not readily available and there is limited information regarding their reliability (Beglinger et al., 2008; Hobson, 2007). Moreover, data relating to their effectiveness for detecting cognitive decline in older adults who have a neurodegenerative disorder had yet to be synthesized. The longstanding use of sorting tests in research and psychological practice suggested a meta-analysis would be useful to determine their effectiveness for detecting cognitive decline in older adults. Study 1 (Chapter 3) involved a meta-analysis of 142 studies that used sorting tests in older adults (≥60 years of age) with and without a neurodegenerative disorder, including dementia and Parkinson’s disease. This study found sorting tests were highly effectively for differentiating between those with and without a neurodegenerative disorder, especially dementia. In addition, their effectiveness seems to rival the Mini Mental Status Examination (MMSE; Mitchell, 2009), suggesting they may provide a viable alternative to this popular screen. Incidentally, the meta-analysis found sorting tests did not reliably differentiate between behavioural-variant fronto-temporal dementia and Alzheimer’s dementia, which has significant clinical implications because they are often used for this purpose (American Psychiatric Association, 2013; Musa et al., 2020; Gustafson, et al., 1998; Possin et al., 2013). Of the different scores that sorting tests yield, the Category (grouping stimuli into categories) and Description (explaining the underlying categories) scores proved to be most effective for screening older adults for cognitive decline. Study 2 (Chapter 4) introduced a newly developed cognitive screen – the QuickSort, which was designed to improve upon existing sorting tests (e.g.,Weigl). The QuickSort uses nine stimuli that need to be sorted by colour, shape and number, with the person additionally being required to explain/describe the basis for their correct sorts. It was designed to be quicker than existing sorting tests because it uses less stimuli and provides an early discontinuation rule for intact performance. The QuickSort also captures different levels of cognitive impairment through the use of additional trials and prompts. Designed for a wide range of older adults, QuickSort scores can be computed even when an examinee finds it too difficult to complete or expressive language problems/low English proficiency prevent a person from explaining their sorts. The QuickSort stimuli, record form and manual are published online. Study 3 (Chapter 5) involved the development of an iPad-compatible version of the QuickSort, called the QuickSort-e. This version of the test was specifically designed to improve the ease with which the test could be administered and scored in a standardized manner, reduce scoring errors and training requirements, and remove the need for physical stimuli and record forms. The QuickSort-e can share patients’ records, which may assist in continuity of care, and store their information for clinical auditing (e.g., to determine patient characteristics) and research purposes. Study 4 (Chapter 6) investigated the user-friendliness, and inter-rater and test-retest reliabilities of the QuickSort. It was administered to older (≥60 years) community-dwelling adults (n = 187) and inpatients referred for neuropsychological assessment (n = 78). The QuickSort was completed in less than two minutes by a cognitively-healthy subgroup (n = 115, defined using MMSE and FAB scores), confirming its brevity. QuickSort scores <2 and ≥17 increased and reduced the likelihood that an older adult was impaired on the MMSE or FAB or both of these screens by a factor of 9.26 (95% CI: 2.96 – 28.75) and 0.16 (95% CI: 0.06 – 0.41), respectively. Furthermore, the accuracy with which the QuickSort detected cognitive impairment improved when the prevalence of impairment on the MMSE and FAB in the specific healthcare setting was additionally considered. Overall, Study 4 found that the QuickSort is quick, easy, reliable, and a valid cognitive screen for detecting cognitive impairment in older adults. Study 5 (Chapter 7) examined the QuickSort in relation to the complex clinical scenario of providing information regarding the lifestyle decision-making capacity of inpatients (LS-DMC; n = 124). In busy healthcare settings clinical interviews are used identify the inpatients needing comprehensive LS-DMC assessments in order to classify them as lacking or not-lacking LS-DMC. Of the information available at the interview stage, which included cognitive screening performances on the MMSE and FAB, the QuickSort best differentiated between those who lacked LS-DMC and those who did not. Low (<2) and high (≥13) QuickSort scores increased or reduced the likelihood that a person lacked LS-DMC by a factor of 65.26 (95% CI: 2.91 – 1463.90) and 0.32 (95%CI: 0.18 – 0.57), respectively. In healthcare settings where many (58%) inpatients lack LS-DMC, the probability of inpatients lacking LS-DMC increased to 99% when their QuickSort scores were <2 and reduced to 30% with scores ≥13. Thus, the QuickSort appears to provide a viable alternative to other cognitive screens that are used at the initial clinical interview stage to provide information regarding inpatients’ LS-DMC. Overall, the rising prevalence of neurodegenerative disorders and associated cognitive decline is increasing the demand for cognitive screens (Connor, 2021), but existing measures are limited by the time they take to administer, their reliability and the accuracy with which they detect cognitive decline (Larner, 2016). Sorting tests are rarely used for screening (Hobson, 2007), but can effectively detect cognitive decline in older adults. The QuickSort is a new sorting test that provides a brief, reliable, and effective alternative to lengthier screens that are used for detecting cognitive impairment in older adults and appears to provide useful preliminary information regarding their LS-DMC.Thesis (Ph.D.) -- University of Adelaide, School of Psychology, 202

    Spinocerebellar Ataxia

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    This book is about spinocerebellar ataxia (SCA), which is among the most challenging pathologies in the neurological landscape. It covers basic concepts, functional classification, and new approaches to medical and non-medical treatment including rehabilitation/palliative care approaches. The volume also describes a wide spectrum of generalities and particularities about various forms of clinical and genetic presentations of ACS that have life-threatening characteristics and long-standing presentation with tremendous variability in presentation and clinical severity. In addition, the book presents important aspects of cerebellar anatomy, nutrition impact, genetic subtypes, and functional classification of medical and non-medical interventions related to stem cells, rehabilitation, and palliative care

    The role of dopamine in learning, movement & motivation

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    The primary aim of the research I have undertaken is to better understand the influence of dopamine on behavior and to build on knowledge of the various roles of dopamine in the healthy brain but also to improve understanding of the deficits affecting patients with Parkinson’s disease (PD), the hallmark of which is dopamine depletion. By testing PD patients on cognitive and motor tasks, we are able to probe the effects of dopamine depletion in humans. Testing PD patients in different medication states also provides a method with which to attempt to tease apart the various roles of dopamine from each other. My first two experiments use the PD model to this end whereas the third experiment utilises a pharmacological manipulation in healthy individuals. The aim of my first experiment was to tease apart the relative contribution of dopamine to learning from its influence on action performance, and by doing this to better understand the deficits which have been observed in PD patients in reinforcement learning tasks. The second experiment focuses on the motor deficits observed in PD. The aim of this study was to test whether these motor deficits can at least in part explained by the deficits in reward sensitivity. The third and final experiment in this thesis uses a pharmacological manipulation in healthy individuals to isolate the role of dopamine in set shifting in the context of a response to cues with negative hedonic valence, with the hope of better understanding the neurobiology underlying pathological behaviours associated with the hyperdopaminergic state

    Wearable Biosensors to Understand Construction Workers' Mental and Physical Stress

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    Occupational stress is defined as harmful physical and mental responses when job requirements are greater than a worker's capacity. Construction is one of the most stressful occupations because it involves physiologically and psychologically demanding tasks performed in a hazardous environment this stress can jeopardize construction safety, health, and productivity. Various instruments, such as surveys and interviews, have been used for measuring workers’ perceived mental and physical stress. However valuable, such instruments are limited by their invasiveness, which prevents them from being used for continuous stress monitoring. The recent advancement of wearable biosensors has opened a new door toward the non-invasive collection of a field worker’s physiological signals that can be used to assess their mental and physical status. Despite these advancements, challenges remain: acquiring physiological signals from wearable biosensors can be easily contaminated from diverse sources of signal noise. Further, the potential of these devices to assess field workers’ mental and physical status has not been examined in the naturalistic work environment. To address these issues, this research aims to propose and validate a comprehensive and efficient stress-measurement framework that recognizes workers mental and physical stress in a naturalistic environment. The focus of this research is on two wearable biosensors. First, a wearable EEG headset, which is a direct measurement of brain waves with the minimal time lag, but it is highly vulnerable to various artifacts. Second, a very convenient wristband-type biosensor, which may be used as a means for assessing both mental and physical stress, but there is a time lag between when subjects are exposed to stressors and when their physiological signals change. To achieve this goal, five interrelated and interdisciplinary studies were performed to; 1) acquire high-quality EEG signals from the job site; 2) assess construction workers’ emotion by measuring the valence and arousal level by analyzing the patterns of construction workers’ brainwaves; 3) recognize mental stress in the field based on brain activities by applying supervised-learning algorithms;4) recognize real-time mental stress by applying Online Multi-Task Learning (OMTL) algorithms; and 5) assess workers’ mental and physical stress using signals collected from a wristband biosensor. To examine the performance of the proposed framework, we collected physiological signals from 21 workers at five job sites. Results yielded a high of 80.13% mental stress-recognition accuracy using an EEG headset and 90.00% physical stress-recognition accuracy using a wristband sensor. These results are promising given that stress recognition with wired physiological devices within a controlled lab setting in the clinical domain has, at best, a similar level of accuracy. The proposed wearable biosensor-based, stress-recognition framework is expected to help us better understand workplace stressors and improve worker safety, health, and productivity through early detection and mitigation of stress at human-centered, smart and connected construction sites.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149965/1/hjebelli_1.pd

    Left-right asymmetry of the human brain: Associations with neurodevelopmental disorders and genetic factors

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    Intelligent technologies for the aging brain: opportunities and challenges

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    Intelligent computing is rapidly reshaping healthcare. In light of the global burden of population aging and neurological disorders, dementia and elderly care are among the healthcare sectors that are most likely to benefit from this technological revolution. Trends in artificial intelligence, robotics, ubiquitous computing, neurotechnology and other branches of biomedical engineering are progressively enabling novel opportunities for technology-enhanced care. These Intelligent Assistive Technologies (IATs) open the prospects of supporting older adults with neurocognitive disabilities, maintain their independence, reduce the burden on caregivers and delay the need for long-term care (1, 2). While technology develops fast, yet little knowledge is available to patients and health professionals about the current availability, applicability, and capability of existing IATs. This thesis proposes a state-of-the-art analysis of IATs in dementia and elderly care. Our findings indicate that advances in intelligent technology are resulting in a rapidly expanding number and variety of assistive solutions for older adults and people with neurocognitive disabilities. However, our analysis identifies a number of challenges that negatively affect the optimal deployment and uptake of IATs among target users and care institutions. These include design issues, sub-optimal approaches to product development, translational barriers between lab and clinics, lack of adequate validation and implementation, as well as data security and cyber-risk weaknesses. Additionally, in virtue of their technological novelty, intelligent technologies raise a number of Ethical, Legal and Social Implications (ELSI). Therefore, a significant portion of this thesis is devoted to providing an early ethical Technology Assessment (eTA) of intelligent technology, hence contributing to preparing the terrain for its safe and ethically responsible adoption. This assessment is primarily focused on intelligent technologies at the human-machine interface, as these applications enable an unprecedented exposure of the intimate dimension of individuals to the digital infosphere. Issues of privacy, integrity, equality, and dual-use were addressed at the level of stakeholder analysis, normative ethics and human-rights law. Finally, this thesis is aimed at providing evidence-based recommendations for guiding participatory and responsible development in intelligent technology, and delineating governance strategies that maximize the clinical benefits of IATs for the aging world, while minimizing unintended risks
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